The University of Southampton
×
Filter your search:
University of Southampton Institutional Repository

A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring

A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring
A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring
805-827
Zilli, Davide
fd263fca-cd6b-48eb-b9be-30e168a51a39
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Zilli, Davide
fd263fca-cd6b-48eb-b9be-30e168a51a39
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc

Zilli, Davide, Parson, Oliver, Merrett, Geoff V. and Rogers, Alex (2014) A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring. Journal of Artificial Intelligence Research, 51, 805-827. (doi:10.1613/jair.4434).

Record type: Article
Text
zilli14a.pdf - Accepted Manuscript
Restricted to Repository staff only
Request a copy

More information

Submitted date: May 2014
e-pub ahead of print date: 30 December 2014
Published date: December 2014
Organisations: Agents, Interactions & Complexity
Learn more about Agents, Interactions & Complexity research Learn more about Cyber Physical Systems research

Identifiers

Local EPrints ID: 373070
URI: http://eprints.soton.ac.uk/id/eprint/373070
DOI: doi:10.1613/jair.4434
PURE UUID: 678d01c2-d0c5-4c2e-920f-82a48d1f53ec
ORCID for Geoff V. Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 06 Jan 2015 09:51
Last modified: 08 Jan 2022 03:02

Export record

Altmetrics

Contributors

Author: Davide Zilli
Author: Oliver Parson
Author: Geoff V. Merrett ORCID iD
Author: Alex Rogers

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Library staff additional information
Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×